David N. Ford

Mental Models Concepts for System Dynamics Research

Although “mental models” are of central importance to system dynamics research and practice, the field has yet to develop an unambiguous and agreed upon definition of them. To begin to address this problem, existing definitions and descriptions of mental models in system dynamics and several literatures related to cognitive science were reviewed and compared. Available definitions were found to be overly brief, general, and vague, and different authors were found to markedly disagree on the basic characteristics of mental models. Based on this review, we concluded that in order to reduce the amount of confusion in the literature, the mental models concept should be “unbundled” and the term “mental models” should be used more narrowly. To initiate a dialogue through which the system dynamics community might achieve a shared understanding of mental models, we propose a new definition of “mental models of dynamic systems” accompanied by an extended annotation that explains the definitional choices made and suggests terms for other cognitive structures left undefined by narrowing the mental model concept. Suggestions for future research that could improve the fields ability to further define mental models are discussed.

Dynamic modeling of product development processes

Successful development projects are critical to success in many industries. To improve project performance managers must understand the dynamic concurrence relationships that constrain the sequencing of tasks as well as the effects of and interactions with resources (such as labor), project scope and targets (such as delivery dates). This article describes a multiple-phase project model which explicitly models process, resources, scope, and targets. The model explicitly portrays iteration, four distinct development activities and available work constraints to describe development processes. The model is calibrated to a semiconductor chip development project. Impacts of the dynamics of development process structures on research and practice are discussed.

Expert knowledge elicitation to improve formal and mental models

Knowledge intensive processes are often driven and constrained by the mental models of experts acting as direct participants or managers. Descriptions of these relationships are not generally available from traditional data sources but are stored in the mental models of experts. Often the knowledge is not explicit but tacit, so it is diAcult to describe, examine, and use. Consequently, improvement of complex processes is plagued by false starts, failures, institutional and interpersonal conflict, and policy resistance. Modelers face diAculties in eliciting and representing the knowledge of experts so that useful models can be developed. We describe and illustrate an elicitation method that uses formal modeling and three description format transformations to help experts explicate their tacit knowledge. We use the method to elicit detailed process knowledge describing the development of a new semiconductor chip. The method improved model accuracy and credibility and provided tools for development team mental model improvement. * c 1998 John Wiley & Sons, Ltd. Syst. Dyn. Rev. 14, 309‐340, (1998) Many public and private sector systems increasingly depend on knowledge intensive processes managed and operated by interdisciplinary teams. These systems are diAcult to manage. Often formal models such as system dynamics models are used to help managers understand the sources of diAculties and design more eAective policies. Typically, the expert knowledge of the people who actually operate the system is required to structure and parameterize a useful model. To develop a useful model that is also credible in the eyes of the managers, however, modelers must elicit from these experts information about system structure and governing policies, and then use this information to develop the model. While many methods to elicit information from experts have been developed, most assist in the early phases of modeling: problem articulation, boundary selection, identification of variables, and qualitative causal mapping. These methods are often used in conceptual modeling, that is, in modeling eAorts that stop short of the development of a formal model that can be used to test hypotheses and proposed policies. The literature is comparatively silent, however, regarding methods to elicit the information required to estimate the parameters, initial conditions, and behavior relationships that must be specified precisely in formal modeling.

Overcoming the 90% Syndrome: Iteration Management in Concurrent Development Projects

Successfully implementing concurrent development to reduce cycle time has proven difficult due to unanticipated iterations. We develop a dynamic project model that explicitly models these interactions to investigate the causes of the “90% syndrome,” a common form of schedule failure in concurrent development. We find that increasing concurrence and common managerial responses to schedule pressure aggravate the syndrome and degrade schedule performance and project quality. We show how understanding of and policies to avoid the 90% syndrome require integration of the technical attributes of the project, the flows of information among participants, and the behavioral decision-making heuristics participants use to respond to unanticipated problems and perturbations.

A real options approach to valuing strategic flexibility in uncertain construction projects

To maximize project value, managers of construction projects must recognize, plan for and strategically manage uncertainty. Current construction planning, estimating, and management practices regarding uncertainty can undervalue projects by failing to exploit opportunities to increase project value, as well as minimize risks. Dynamic uncertainties are described as project conditions that cannot be resolved adequately through improved description or planning for pre-project strategy selection. A real options approach is proposed for proactively using strategic flexibility to recognize and capture project values hidden in dynamic uncertainties. An example of a proposal for a toll road project demonstrates a method of valuing managerial flexibility to evaluate and select strategies. Impacts of real options in other domains, along with this example, are the basis for concluding that using a structured real options approach in construction management can increase returns through improved project planning and management. Potential impacts of the use of real options are discussed, and challenges in valuing real options in construction projects are identified as the basis for future research.

A behavioral approach to feedback loop dominance analysis

Feedback loop dominance is a critical tool in explaining how structure drives behavior. Current analytic tools for loop dominance analysis are tacit, not codified, unable to accurately identify dominant loops or inapplicable to most models. Most loop dominance analysis tools focus on model structure to link structure and behavior. We use a behavioral perspective to define dominance, improve descriptions of behavior patterns and identify two important and incompletely developed areas of feedback analysis: simultaneous dominance by multiple loops and shadow loop structures. An analytic procedure is presented, illustrated and compared to an alternative analysis method. An evaluation of the behavioral approach is the basis for identifying new issues and future research opportunities. Copyright

Mental models concepts revisited: some clarifications and a reply to Lane

This article revisits a conceptual definition of “mental models of dynamic systems” proposed for use in system dynamics research by Doyle and Ford and commented on by Lane. Lanes proposed amendments to the definition are discussed in turn, with particular attention to the history and appropriate use of the term “cognitive map.” A revised definition informed by Lanes commentary is offered. Copyright

The Liar's Club: Concealing Rework in Concurrent Development:

Successfully implementing concurrent development has proven difficult for many organizations. However, many theories addressing concurrent development treat either technical aspects of the development process (e.g., precedence relationships) or behavioral issues (e.g., creating effective cross-functional teams), but not their linkages. We argue that much of the complexity of concurrent development—and the implementation failures that plague many organizations—arises from interactions between the technical and behavioral dimensions. We use a dynamic project model that explicitly represents these interactions to investigate how a “Liars Club”—concealing known rework requirements from managers and colleagues—can aggravate the “90% syndrome,” a common form of schedule failure, and disproportionately degrade schedule performance and project quality. We discuss the role of the incentives on and behavior of engineers and managers in concurrent development failure and explore policies to improve project performance.

Adapting real options to new product development by modeling the second Toyota paradox

Uncertainty in product development projects creates significant challenges for managers who are under intense competitive pressures to increase product quality, while reducing development time and costs. Traditional wisdom dictates the early selection of a single design in order to freeze interfaces between product subsystems so that team members can work effectively in parallel, resulting in more productive product development efforts. Prior research, however, uncovered a paradoxical case. Toyota Motor Corporation achieves the fastest development times in its industry by intentionally delaying alternative selection, a strategy termed set-based development. The current work adapts real options concepts to product development management to partially explain this paradox. A formal simulation model is used to show that converging too quickly or too slowly degrades project value. Furthermore, the model demonstrates that the wisdom of set-based strategies can be explained by the application of a real options approach to product development management. Implications for managers and directions for future work are discussed.

A new perspective on Renn and Klinke's approach to risk evaluation and management

In this paper we discuss Renn and Klinkes approach for risk evaluation and selection of risk management strategies. The main focus in the discussion is the foundational basis and the understanding of what risk is, and how a different foundational basis may simplify and improve the characterization of risk. We will present and discuss an alternative set of characteristics, and give some recommendations with respect to selection of risk management strategies based on different values or magnitudes of these characteristics. We believe that the main focus when describing and managing risk should be the potential consequences, represented by observable quantities, and the uncertainty related to their future values.